Abstract
This paper studies performance of various classifiers for Word Sense Disambiguation considering different training conditions. Our preliminary results indicate that the number and distribution of training examples has a great impact on the resulting precision. The Naïve Bayes method emerged as the most adequate classifier for disambiguating words having few examples. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Pancardo-Rodríguez, A., Montes-y-Gómez, M., Villaseñor-Pineda, L., & Rosso, P. (2005). A mapping between classifiers and training conditions for WSD. In Lecture Notes in Computer Science (Vol. 3406, pp. 246–249). Springer Verlag. https://doi.org/10.1007/978-3-540-30586-6_27
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
